MPRAsnakeflow experiment QC report

Low correlation warning!

At least one (not all) Spearman correlations across replicates is low using a barcode threshold per oligo of 10! Spearman correlation of DNA is 0.95, RNA is 0.60 and DNA/RNA ratio is 0.65. Minimum allowed values are 0.85, 0.95, and 0.75.

Overall quality metrics

Table explanation

median rna read count: Median of RNA read count for oligos that passed filtering to determine sufficient coverage in terms of read count. Value is the median of all replicates.
median barcodes passing filtering: Median number of barcodes across tested sequences that passed filtering to determine if there was sufficient barcode to oligo coverage. Value is the median of all replicates.
pearson correlation: The correlation of log2 RNA/DNA ratios across tested sequences as a measure of replicable activity signal. Value is the median of replicate comparisons using only oligos with >= 10 barcodes.
fraction oligos passing: Fraction of tested sequences that passed filtering of the mappable sequences to determine if the designed library was sufficiently recovered. Value is the median of all replicates and using only oligos with >= 10 barcodes.

median barcodes passing filtering	median rna read count	pearson correlation	fraction oligos passing
67	641	0.85	0.92

DNA over RNA counts

Plotting normalized counts of DNA vs RNA (median across replicates). Only oligos within all replicates are shown. We should see a variation within the RNA count data (along the y axis). If count data between RNA and DNA is highly correlated (e.g. follows the identity line) there is no variation between designed oligos. This is an indication that RNA is inflated with DNA and the DNA digestion before creating cDNA did not work as expected.

Oligo correlation

Oligo correlation plots of DNA, RNA and DNA/RNA ratios across replicates. First tab shows plots using (in average) 54114 oligos with a minimum number of 10 barcodes. Second tab shows all 58440 oligos that have assigned barcodes.

Condition	A	B	#Oligos A	#Oligos B	#Oligos Joined	DNA spearman	RNA spearman	Ratio spearman	DNA log2 pearson	RNA log2 pearson	Ratio log2 pearson
A549	1	2	54791	54705	54328	0.96	0.63	0.67	0.96	0.82	0.84
A549	1	3	54791	54775	54367	0.96	0.64	0.67	0.96	0.83	0.85
A549	1	4	54791	54339	54066	0.96	0.62	0.67	0.96	0.82	0.84
A549	1	5	54791	54333	54057	0.96	0.60	0.65	0.95	0.81	0.84
A549	2	3	54705	54775	54305	0.96	0.66	0.71	0.96	0.86	0.88
A549	2	4	54705	54339	54045	0.96	0.64	0.70	0.96	0.84	0.86
A549	2	5	54705	54333	54026	0.96	0.63	0.70	0.95	0.84	0.87
A549	3	4	54775	54339	54054	0.96	0.65	0.71	0.96	0.85	0.87
A549	3	5	54775	54333	54061	0.96	0.64	0.70	0.96	0.86	0.87
A549	4	5	54339	54333	53831	0.95	0.61	0.70	0.95	0.84	0.86

Condition	A	B	#Oligos A	#Oligos B	#Oligos Joined	DNA spearman	RNA spearman	Ratio spearman	DNA log2 pearson	RNA log2 pearson	Ratio log2 pearson
A549	1	2	58718	58727	58502	0.95	0.60	0.63	0.91	0.73	0.73
A549	1	3	58718	58716	58480	0.95	0.61	0.64	0.91	0.74	0.74
A549	1	4	58718	58649	58423	0.95	0.58	0.64	0.90	0.71	0.73
A549	1	5	58718	58638	58413	0.95	0.57	0.62	0.90	0.72	0.73
A549	2	3	58727	58716	58497	0.95	0.63	0.67	0.91	0.77	0.77
A549	2	4	58727	58649	58436	0.95	0.61	0.66	0.90	0.74	0.75
A549	2	5	58727	58638	58433	0.95	0.60	0.66	0.90	0.75	0.76
A549	3	4	58716	58649	58426	0.95	0.61	0.67	0.90	0.75	0.76
A549	3	5	58716	58638	58421	0.95	0.61	0.66	0.90	0.77	0.77
A549	4	5	58649	58638	58378	0.94	0.58	0.66	0.90	0.74	0.76

Experiment statistic

The total number of oligos in this experiment is 59308 (defined by the assignment) with 10381929 associated barcodes.

In average across replicates we see 58690 from 4912794 average barcodes in the count data and around 1166296 barcodes where not in the assignment.

condition	replicate	oligos dna/rna	matched barcodes	unknown barcodes dna/rna	% matched barcodes	total dna counts	total rna counts	avg dna counts per bc	avg rna counts per bc	avg dna/rna barcodes per oligo
A549	1	58718	5078773	1200052	80.89	201932147	92956866	32.16	14.80	86.49
A549	2	58727	5000998	1194821	80.72	199108959	100483452	32.14	16.22	85.16
A549	3	58716	5121001	1231584	80.61	202834242	83176133	31.93	13.09	87.22
A549	4	58649	4666533	1093679	81.01	188881416	77523105	32.79	13.46	79.57
A549	5	58638	4696666	1111345	80.87	189555867	75199143	32.64	12.95	80.10

Experiment	Barcodes	Counts	Average counts	Assigned barcodes	Assigned counts	Average assigned counts	Fraction assigned barcodes	Fraction assigned counts
A549.1.DNA	15667758	299222797	19.10	9522795	251662451	26.43	0.61	0.84
A549.2.DNA	15667758	299222797	19.10	9522795	251662451	26.43	0.61	0.84
A549.3.DNA	15667758	299222797	19.10	9522795	251662451	26.43	0.61	0.84
A549.4.DNA	15667758	299222797	19.10	9522795	251662451	26.43	0.61	0.84
A549.5.DNA	15667758	299222797	19.10	9522795	251662451	26.43	0.61	0.84
A549.1.RNA	7637312	95973168	12.57	5145721	79451916	15.44	0.67	0.83
A549.2.RNA	7707693	103808893	13.47	5067519	85874822	16.95	0.66	0.83
A549.3.RNA	7658028	85918859	11.22	5190860	71060361	13.69	0.68	0.83
A549.4.RNA	6935335	80063496	11.54	4726069	66157073	14.00	0.68	0.83
A549.5.RNA	6998535	77706392	11.10	4757847	64309380	13.52	0.68	0.83

Histograms barcodes per oligo, counts per barcode

Histogramm of number of barcodes per oligo and the number of counts per barcode devidied by DNA and RNA. Median is red, mean is blue.

Activity

Violin and box plots of the log2 fold change of all oligos by the assay. Grouped by labels if set, otherwise NA. First tab shows plots using (in average) 54114 oligos with a minimum number of 10 barcodes. Second tab shows all 58440 oligos that have assigned barcodes.

--- title: "MPRAsnakeflow experiment QC report" page-layout: full format: html: code-fold: true code-tools: true embed-resources: true anchor-sections: true smooth-scroll: true toc: true theme: light: flatly dark: darkly jupyter: python3 --- ```{python} #| echo: false # Imports from IPython.display import display, Image, Markdown import pandas as pd import json ``` ```{python} #| tags: [parameters] #| echo: false ``` ```{python} #| echo: false oligo_cor_merged_table_thresh = pd.read_csv(f"{workdir}/{statistics_all_oligo_cor_thresh}", sep='\t') oligo_cor_merged_table_all = pd.read_csv(f"{workdir}/{statistics_all_oligo_cor_all}", sep='\t') dna_spear = oligo_cor_merged_table_thresh['DNA_spearman'].min() rna_spear = oligo_cor_merged_table_thresh['RNA_spearman'].min() ratio_spear = oligo_cor_merged_table_thresh['Ratio_spearman'].min() ``` ```{python} #| echo: false threshold_dna_spear = 0.85 threshold_rna_spear = 0.95 threshold_ratio_spear = 0.75 important = dna_spear < threshold_dna_spear and rna_spear < threshold_rna_spear and ratio_spear < threshold_ratio_spear warning = dna_spear < threshold_dna_spear or rna_spear < threshold_rna_spear or ratio_spear < threshold_ratio_spear if important: display(Markdown(f''' ::: {"{.callout-important}"} ## Low correlation! Spearman correlation across replicates is low using a barcode threshold per oligo of {thresh}! Spearman correlation of DNA is {dna_spear:.2f}, RNA is {rna_spear:.2f} and DNA/RNA ratio is {ratio_spear:.2f}. Minimum allowed values are {threshold_dna_spear:.2f}, {threshold_rna_spear:.2f}, and {threshold_ratio_spear:.2f}. ::: ''')) elif warning: display(Markdown(f''' ::: {"{.callout-warning}"} ## Low correlation warning! At least one (not all) Spearman correlations across replicates is low using a barcode threshold per oligo of {thresh}! Spearman correlation of DNA is {dna_spear:.2f}, RNA is {rna_spear:.2f} and DNA/RNA ratio is {ratio_spear:.2f}. Minimum allowed values are {threshold_dna_spear:.2f}, {threshold_rna_spear:.2f}, and {threshold_ratio_spear:.2f}. ::: ''')) else: display(Markdown(f''' ::: {"{.callout-tip}"} ## Good Spearman correlation across replicates! The data has a good Spearman correlations across replicates using a barcode threshold per oligo of {thresh}! Spearman correlation of DNA is {dna_spear:.2f}, RNA is {rna_spear:.2f} and DNA/RNA ratio is {ratio_spear:.2f}. ::: ''')) ``` # Overall quality metrics <details> <summary>Table explanation</summary> - **median rna read count:** Median of RNA read count for oligos that passed filtering to determine sufficient coverage in terms of read count. Value is the median of all replicates. - **median barcodes passing filtering:** Median number of barcodes across tested sequences that passed filtering to determine if there was sufficient barcode to oligo coverage. Value is the median of all replicates. - **pearson correlation:** The correlation of log2 RNA/DNA ratios across tested sequences as a measure of replicable activity signal. Value is the median of replicate comparisons using only oligos with >= `{python} thresh` barcodes. - **fraction oligos passing:** Fraction of tested sequences that passed filtering of the mappable sequences to determine if the designed library was sufficiently recovered. Value is the median of all replicates and using only oligos with >= `{python} thresh` barcodes. </details> ```{python} #| echo: false with open(f"{workdir}/{qc_metrics_file}", 'r') as file: qc_data = json.load(file) df_qc = pd.DataFrame([qc_data]) df_qc.columns = df_qc.columns.str.replace("_", " ", regex=False) #df_qc.columns = ['Value'] df_qc.style.hide(axis="index").format(precision=2) ``` # DNA over RNA counts Plotting normalized counts of DNA vs RNA (median across replicates). Only oligos within all replicates are shown. We should see a variation within the RNA count data (along the y axis). If count data between RNA and DNA is highly correlated (e.g. follows the identity line) there is no variation between designed oligos. This is an indication that RNA is inflated with DNA and the DNA digestion before creating cDNA did not work as expected. ::: {.panel-tabset} ## Threshold `{python} thresh` ```{python} #| echo: false Image(f"{workdir}/{dna_over_rna_thresh_plot}") ``` ## All ```{python} #| echo: false Image(f"{workdir}/{dna_over_rna_plot}") ``` ::: # Oligo correlation Oligo correlation plots of DNA, RNA and DNA/RNA ratios across replicates. First tab shows plots using (in average) `{python} "%d" % oligo_cor_merged_table_thresh['number_Oligos_Joined'].mean()` oligos with a minimum number of `{python} thresh` barcodes. Second tab shows all `{python} "%d" % oligo_cor_merged_table_all['number_Oligos_Joined'].mean()` oligos that have assigned barcodes. ```{python} #| echo: false def filter_table(df): df = df.round(2).drop(columns=['DNA_pearson','RNA_pearson', 'Ratio_pearson', 'fraction_Oligos_ReplicateA', 'fraction_Oligos_ReplicateB', 'NormSymmetry']) df.columns = df.columns.str.replace("_", " ", regex=False).str.replace("Replicate", " ", regex=False).str.replace("number ", "#", regex=False) return(df) ``` ::: {.panel-tabset} ## Threshold `{python} thresh` ```{python} #| echo: false oligo_cor_merged_table_thresh_filter = filter_table(oligo_cor_merged_table_thresh) oligo_cor_merged_table_thresh_filter.style.hide(axis="index").format( precision=2) ``` ## All ```{python} #| echo: false oligo_cor_merged_table_all_filter = filter_table(oligo_cor_merged_table_all) oligo_cor_merged_table_all_filter.style.hide(axis="index").format( precision=2) ``` ::: ::: {.panel-tabset} ## Threshold `{python} thresh` ::: {.panel-tabset} ### DNA ```{python} #| echo: false display(Image(f"{workdir}/{dna_oligo_coor_min_thre_plot }")) ``` ### RNA ```{python} #| echo: false display(Image(f"{workdir}/{rna_oligo_coor_min_thre_plot}")) ``` ### Ratio ```{python} #| echo: false display(Image(f"{workdir}/{ratio_oligo_min_thre_plot}")) ``` ::: ## All ::: {.panel-tabset} ### DNA ```{python} #| echo: false display(Image(f"{workdir}/{dna_oligo_coor_plot}")) ``` ### RNA ```{python} #| echo: false display(Image(f"{workdir}/{rna_oligo_coor_plot}")) ``` ### Ratio ```{python} #| echo: false display(Image(f"{workdir}/{ratio_oligo_coor_plot}")) ``` ::: ::: # Experiment statistic ```{python} #| echo: false df_merged = pd.read_csv(f"{workdir}/{statistics_all_merged}", sep='\t').round(2) n_oligos = df_merged['oligos design'].iloc[0] n_barcodes = df_merged['barcodes design'].iloc[0] avg_oligos = df_merged['oligos dna/rna'].mean() avg_matched_barcodes = df_merged['matched barcodes'].mean() avg_unknown_barcodes = df_merged['unknown barcodes dna/rna'].mean() ``` The total number of oligos in this experiment is `{python} f"{n_oligos}"` (defined by the assignment) with `{python} f"{n_barcodes}"` associated barcodes. In average across replicates we see `{python} f"{avg_oligos:.0f}"` from `{python} f"{avg_matched_barcodes:.0f}"` average barcodes in the count data and around `{python} f"{avg_unknown_barcodes:.0f}"` barcodes where not in the assignment. ::: {.panel-tabset} # DNA/RNA merged ```{python} #| echo: false df_merged.drop(columns=['oligos design','barcodes design', 'barcodes dna/rna'], inplace=True) df_merged.style.hide(axis="index").format( precision=2) ``` # DNA/RNA individual ```{python} #| echo: false df_single = pd.read_csv(f"{workdir}/{statistics_all_single}", sep='\t').round(2) df_single.style.hide(axis="index").format( precision=2) ``` ::: # Histograms barcodes per oligo, counts per barcode Histogramm of number of barcodes per oligo and the number of counts per barcode devidied by DNA and RNA. Median is red, mean is blue. ::: {.panel-tabset} # Number of barcodes per oligo ```{python} #| echo: false display(Image(f"{workdir}/{counts_per_oligo}")) ``` # Number of counts per DNA barcode ```{python} #| echo: false display(Image(f"{workdir}/{counts_per_bc_dna}")) ``` # Number of counts per RNA barcode ```{python} #| echo: false display(Image(f"{workdir}/{counts_per_bc_rna}")) ``` ::: # Activity Violin and box plots of the log2 fold change of all oligos by the assay. Grouped by labels if set, otherwise `NA`. First tab shows plots using (in average) `{python} "%d" % oligo_cor_merged_table_thresh['number_Oligos_Joined'].mean()` oligos with a minimum number of `{python} thresh` barcodes. Second tab shows all `{python} "%d" % oligo_cor_merged_table_all['number_Oligos_Joined'].mean()` oligos that have assigned barcodes. ::: {.panel-tabset} ## Threshold `{python} thresh` ```{python} #| echo: false display(Image(f"{workdir}/{activity_thresh}")) ``` ## All ```{python} #| echo: false display(Image(f"{workdir}/{activity_all}")) ``` :::